Learn how to create statistical plots, including histograms and scatter plots.
- [Instructor] Statistical plots allow viewers…to identify outliers, visualize distributions,…deduce variable types…and discover relationships and core relations…between variables in a dataset.…In this course I'm going to show you how…to use statistical plots…to visually detect outliers,…deduce variable distribution and type,…and uncover relationships…and core relation between variables.…Histograms are very simple plots…that are used to show variable distribution.…Scatterplots on the other hand,…are used to show relationships between variables.…
Scatterplot matrices show core relations between variables…and box plots show variable spread…and are useful for outlier detection.…Let me show you how to create these in Python.…In this demonstration,…we're going to be using npi in Panda's library.…From Pandas we want to import the tool…for scatterplot matrices.…So we'll say from Pands.tool.plotting.…This is the module that has the tool.…
We'll say import scatter_matrix.…And we're also going to be using Matplotlib…and Seaborn like we have throughout this chapter.…
AuthorLillian Pierson, P.E.
- Getting started with Jupyter Notebooks
- Visualizing data: basic charts, time series, and statistical plots
- Preparing for analysis: treating missing values and data transformation
- Data analysis basics: arithmetic, summary statistics, and correlation analysis
- Outlier analysis: univariate, multivariate, and linear projection methods
- Introduction to machine learning
- Basic machine learning methods: linear and logistic regression, Naïve Bayes
- Reducing dataset dimensionality with PCA
- Clustering and classification: k-means, hierarchical, and k-NN
- Simulating a social network with NetworkX
- Creating Plot.ly charts
- Scraping the web with Beautiful Soup
Skill Level Beginner
1. Data Munging Basics
2. Data Visualization Basics
3. Basic Math and Statistics
4. Dimensionality Reduction
Explanatory factor analysis6m 39s
5. Outlier Analysis
6. Cluster Analysis
7. Network Analysis with NetworkX
8. Basic Algorithmic Learning
9. Web-based Data Visualizations with Plotly
10. Web Scraping with Beautiful Soup
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